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The Impact of Rising Inequality on Health at Birth

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The Impact of Rising Inequality on Health at Birth The Impact of Rising Inequality on Health at Birth Anna Aizer Brown University and NBER Florencia Borrescio Higa Brown University Hernan Winkler World Bank May 2013 Abstract: The income distribution has widened significantly over the past 40 years, primarily due to skill biased technological change which has disproportionately raised the income of the most highly skilled. A major concern over rising inequality is its potential to reduce intergenerational mobility, leading to even greater inequality in the next generation. We estimate the impact of rising inequality over the period 1970-2000 on offspring health at birth, a measure of human capital that has been shown to be highly correlated with future education, IQ and income. We define inequality three ways: as a group-level measure (the Gini coefficient for each county), as an individual-level measure of relative deprivation and as an ordinal measure of rank. We find that including a modest set of controls reduces the negative relationship between aggregate measures of inequality and health, and limiting variation to changes in inequality over time within an area or instrumenting for inequality eliminates it completely. However, this null result likely reflects heterogeneity in the effect of rising inequality. When we estimate the impact of relative deprivation or rank on newborn health, we find negative and significant effects. Together these results suggest that increases in inequality in the current generation may lead to reduced intergenerational mobility and greater levels of inequality in the next generation. 1 I. Introduction Income inequality has been on the rise in most industrialized nations since the 1970s. In the US, for example, the Gini coefficient increased steadily from .39 in 1970 to .47 by 2010. There has been considerable discussion of the causes of the rise in income inequality. Most research based on developed countries points to the increase in skill-biased technological change and globalization as the most important factors.1 In this paper we consider one potential consequence of rising inequality. Specifically, we estimate the impact of inequality on health at birth – a measure of the initial human capital of the next generation. We focus on health at birth for multiple reasons. First, newborn health is sensitive to changes in short term conditions (eg, Almond, 2006). This makes it easier to isolate the economic conditions affecting health. Second, health at birth has been shown to be an important determinant of long term outcomes such as educational attainment, IQ and earnings (Black, Devereaux and Salvanes, 2007). Third, individual-level data on birth outcomes has been collected and reported consistently at a local geographic level (county) for the period 1970-2010, allowing one to estimate the impact of increases in inequality and relative income at a local level on individual outcomes. Finally, by examining the impact of inequality on newborn health, we can learn not only about how inequality affects health, but how it might affect intergenerational mobility and inequality of the next generation. In our estimates of the impact of rising inequality on health at birth, we define inequality in two ways. First, we define it as the Gini coefficient for the local area (state or county). This measure is common to all individuals in the area. Unlike much of the existing empirical work that relies on cross sectional variation, we utilize a 30 year panel of data which allow us to 1 Between 1979 and 2002, the causal return to education increased by 40 percent (Deschenes, 2006). 2 include area fixed effects, thereby limiting variation to that within an area over time and reducing potential omitted variable bias. Another contribution is that we instrument for inequality (Boustan, Ferreira, Winkler and Zolt, 2012). The instrument allows us to isolate the change in the local income distribution that is driven by national shifts in the income distribution over time, not changes in the underlying composition of the area. More specifically, we construct an instrument for local, (state or county-level), distribution of income by holding local area income fixed at the 1970 distribution and match this initial distribution to national patterns in income growth for different points in the distribution of income.2 In initial results using aggregated data, we find a negative relationship between the Gini coefficient and newborn health (birth weight and an indicator for low birth weight). However, as we include even a parsimonious set of controls, the effect declines in magnitude and when we include area fixed effects thereby limiting variation to that within an area over time and/or instrument for the Gini, the estimated effect is neither large nor significant. When we repeat the analysis with individual level data that includes maternal income, we find the same pattern. This is consistent with either no causal effect of inequality on health, or significant heterogeneity in the effect that, in the aggregate, leads to no observed effect. To help interpret this effect, we turn to two individual level measures of relative income. The first is the Yitzhaki measure of relative deprivation and it reflects the average distance 2 For example, consider two counties, A and B. In 1970, county A had a disproportionate share of women in the bottom quartile of the (national) education distribution at the time, while county B had a disproportionate share of women in the top quartile of the (national) education distribution. By 1980, the distribution of education county A had changed so that it is more similar to the distribution of education in county B. However the instrument for the distribution of income in county A is calculated by holding the distribution of education fixed at the 1970 level and then predicting the income distribution based on national trends in income growth for the initial distribution of education. 3 between an individual’s own income and the income of those above her. When we estimate the impact of relative deprivation on newborn health including own absolute income, area fixed effects and instrumenting for relative deprivation, we find that the one’s relative deprivation is negatively related to newborn health. The second measure of relative income is an ordinal measure: rank based on the income of new mothers in the state. We find that conditional on absolute income, rank is related to newborn health and that the effect is non-linear (with greater effects for those at the bottom) and greater in areas characterized by larger income dispersion. These findings suggest that for the poor, rising inequality reduces the initial human capital of the next generation, thereby reducing intergenerational mobility for the most disadvantaged. II. Background A. Inequality and the Intergenerational Transmission of Economic Status There is a considerable theoretical literature on the relationship between income inequality and intergenerational mobility, largely in the macroeconomics literature (see Piketty, 1998 for a review). In general, the research suggests that greater inequality should reduce intergenerational mobility and growth. There are a number of mechanisms. The first has to do with imperfect credit markets and investments in human capital. Galor and Moav(2004) posit that because of credit constraints, in an unequal society there will be suboptimal investment in the human capital of the next generation (see Burtless and Jenckes, 2003, for a more microeconomic perspective). Not only will this lead to greater inequality in the next generation, but also to reduced overall growth. A second potential mechanism relates to segregation. Durlauf (1996) argues that greater inequality will lead to greater segregation by income which will have the effect of reducing the 4 human and social capital of the next generation. Finally, models of statistical discrimination can also explain how greater inequality in one generation will lead to reduced mobility and increased inequality in the next generation. In the presence of both inequality statistical discrimination, if employers discriminate in their hiring of the relatively disadvantaged, this will lead to reduced human capital investments among the discriminated group (the disadvantaged) and even greater inequality in the next generation (Arrow, 1973, Piketty, 1998). While there is considerable theoretical work on this topic, the empirical work characterizing the relationship between inequality and intergenerational mobility is relatively under-developed. Corak (2012) shows that across OECD countries, there is a strong correlation between measures of inequality (the Gini) and intergenerational transmission of earnings. While the evidence he presents is suggestive, questions remain. First is the question of whether and to what extent this relationship exists at a more micro level. Second is the question of what underlies this relationship and whether it can be characterized as causal. In this paper we attempt to shed greater light on this by estimating whether inequality affects the initial human capital of the next generation – thereby providing a mechanism by which inequality of one generation may lead to lower intergenerational mobility. We do so in the context of newborn health. While there does not yet appear to be any empirical analysis looking at this specific question, there is a substantial empirical literature looking at inequality and health more generally which we review below. B. Inequality, Relative Income and Health 5 Inequality and relative income or relative deprivation are closely related but distinct concepts. Areas characterized by greater inequality have higher relative deprivation. However, inequality characterizes an entire group of individuals, whereas relative income or deprivation is specific to an individual within a group. There are three reasons why inequality could be related to health. The first has to do with non-linearities in the production of health. If maternal income produces child health (see Case, Lubotsky and Paxson, 2002), but is marginally more productive at low levels of income than at high levels, then an increase in inequality would reduce average health.
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